A challenging issue as regards implementing FAIR is the question of how the process of creating interoperable research data can be made as simple as possible. The FAIR Digital Object (FDO) concept seems to emerge as a promising approach to solving the problem. However, reference implementations and show cases are strongly required for this. Therefore, on May 18-19, 2020, the GO FAIR Implementation Network on “Cross-Domain Interoperability of Heterogeneous Research Data (GO Inter)” organized a hackathon on implementing the FAIR Digital Object (FDO) concept to cross-domain interoperability use cases.

The hackathon took place as an online meeting with 21 participants from 15 organizations. The goal of the hackathon was to discuss showcases that demonstrate how cross-domain data searching & linking, semantic artifacts and data sharing across the entire research life cycle can be modeled and implemented as FDOs, by broadly applying existing Semantic Web technologies and approaches. The goal moreover was to contribute to the common understanding of the FDO concept from different perspectives as a foundation to converge different views to FDO .

The participants were welcomed by Peter Mutschke (GESIS – Leibniz Institute for the Social Sciences / coordinator of GO Inter). In his introductory talk he introduced the idea of a FDO implementation profile which explicitly express how the 15 FAIR principles are addressed (by the use of machine-actionable semantic predicates, as proposed by Luiz Bonino [1], and maturity level indicators saying how far the principle in question is covered, as proposed by RDA [2]). Afterwards, Erik Schultes (GO FAIR) gave an inspiring ignition talk on the FAIR Molecule resulting from a hackathon in January on how to come to a generic FDO model of molecular structures. In the following Luiz Bonino (GO FAIR) gave a presentation of the current state of the FAIR Digital Object Framework (FDOF) and the proposed semantic predicates [2], which are the basis for the FAIR Molecule as well.

Based on these talks and the overall discussion of the FDO concept the participants split into three working groups to discuss how to model FDO for specific use cases:

• One working group addressed data sharing in the Educational and Social Sciences where data is handed from one stakeholder to another throughout the entire data lifecycle by using different proprietary software solutions for data management leading to heterogeneous (meta)data. The main objective here is to build an open API based on the FDO concept for the entire end-to-end workflow of data exchange (to foster automated data verification, access rights management, tracing the provenance of data, metadata enrichment and finally data interoperability). Thus, the goal here is to make the process of creating interoperable research data as simple as possible, and FDOs are a promising approach for this. The group identified a number of tasks capturing the use case and started to describe workflow stories of data sharing across the three participating institutions (GESIS, DIPF, DANS). The next step for the WG is to provide some example data from GESIS (survey data), DIPF (Pisa/Assessment data), and DANS to build a data corpus to work on.
• A second group focused on the question of how semantic artifacts can be modeled as FDOs, given the challenge that vocabularies used for (meta)data also need to be FAIR so that users and machines can fully understand the meaning and context of the terms used [3]. However, semantic artefacts are in many cases not easy to find and understandable only to the community where they were developed, especially interoperability of vocabulary services is still a major challenge [4]. The group started to build a dummy csv table containing hourly measurements of lead concentration in the river Thames and semantic artefacts to represent the measurements by using two different ontologies (Sensor Network Ontology (OWL), EnvThes (SKOS)). The group started to create a FDOF representation for the measurements and for the two semantic artefacts, and built example Juypter notebooks demonstrating how to interlink semantic artifacts with data.
• The third group was about implementing FDO to the FAIR Funding Cycle. The idea here is to streamline the creation and sharing of FAIR data throughout the entire research funding cycle, from the call for proposals, through the data management across different stakeholders to describe, archive, register and publish the data, to the final step of reporting research results and data back to the funder. FDOs will be used as the minimal, open standard for data exchange in this cycle. The challenge here is to get the different stakeholders involved, which is also the next step for this group.

All participants agreed to continue their work on the aforementioned issues. On July 1-2 GO Inter will organize a follow-up hackathon to continue the work.

[1] L. Bonino: Internet of fair data and services – Center of the hourglass, GEDE Paris Workshop, Oct. 2019
[2] RDA (2020): FAIR Data Maturity Model. Specification and Guidelines
[3] A. Jacobsen et al. (2020): FAIR Principles: Interpretations and Implementation Considerations, Data Intelligence 2(1-2)
[4] FAIRsFAIR (2020): D2.2 FAIR Semantics: First recommendations. https://doi.org/10.5281/zenodo.3707985